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• A method to quantify atomic resolution first moment STEM images was developed. • The images are modelled as a superposition of derivatives of 2D Lorentzian peaks. • The unknown parameters are estimated using the least squares estimator. • The estimated parameters allow determination of atom counts in each atomic column. • First moment STEM imaging shows promise compared to HAADF STEM for atom counting. Through a simulation-based study we develop a statistical model-based quantification method for atomic resolution first moment scanning transmission electron microscopy (STEM) images. This method uses the uniformly weighted least squares estimator to determine the unknown structure parameters of the images and to isolate contributions from individual atomic columns. In this way, a quantification of the projected potential per atomic column is achieved. Since the integrated projected potential of an atomic column scales linearly with the number of atoms it contains, it can serve as a basis for atom counting. The performance of atom counting from first moment STEM imaging is compared to that from traditional HAADF STEM in the presence of noise. Through this comparison, we demonstrate the advantage of first moment STEM images to attain more precise atom counts. Finally, we compare the integrated potential extracted from first-moment images of a wedge-shaped sample to those values from the bulk crystal. The excellent agreement found between these values proves the robustness of using bulk crystal simulations as a reference library. This enables atom counting for samples with different shapes by comparison with these library values. [ABSTRACT FROM AUTHOR]